Residual Scaling: In EDSR [1]}, Lim et al. found that increasing the feature maps, i.e., channel dimension, above a certain level would make the training procedure numerical unstable. To solve such issues, they adopted the residual scaling [2]}, where the residuals are scaled down by multiplying a constant between 0 and 1 before adding them to the main path. With the help of this residual scaling method, the model performance can be further improved.
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